This paper explores the phenomenon of managerial overoptimism, focusing on the cognitive underpinnings of the mechanisms that generate this bias. It develops a formal model of probability estimation that is inspired by the biological (cognitive neuroscience) evidence on associative information processing in the brain. The model is able to make novel, testable predictions about managerial overoptimism. It is able to parse out three mechanisms that could lead to overoptimism, as well as predict boundary conditions on when these effects should be observed and when the opposite (a pessimistic bias) should be observed instead. Furthermore, it predicts that under certain conditions, attempts by managers to “debias” their estimates might exacerbate the overoptimistic bias.

Mental models, reflecting interdependencies among managerial choice variables, are not always correctly specified. Mental models can be underspecified, missing interdependencies, or overspecified, containing non-existent interdependencies. Using a simulation model, we find that under- and overspecification have opposite effects on exploration, and thereby performance. The effects are also opposite depending on whether the manager controls all choice variables. The mechanism underlying our results is a feedback loop: misspecificed mental models influence managerial learning about the effectiveness of choices; this learning guides how the environment is explored, which in turn affects which information will be generated for future learning. We explore implications of these results for strategic management and introduce the notion of “cognitive fit” between the mental model of the decision maker and the strategic environment.

This paper proposes an approach for modeling strategic interactions that incorporates the costs to firms of changing their strategies. The costs associated with strategy modifications, which we term “repositioning costs,” are particularly relevant to interactions involving grand strategies. Repositioning costs can critically affect competitive dynamics and, consequently, the implications of strategic interaction for strategic choice. While the literature broadly recognizes th eir importance, game-theoretic treatments at the grand strategy level, with very limited exceptions, have not focused on them. We argue for greater recognition of repositioning costs and demonstrate the fertility of this approach with a simple model th at illustrates how repositioning costs may facilitate differentiation and affect the value of dynamic capabilities.

In research on strategic networks, the addition or deletion of ties is the primary mechanism through which firms alter their networks. Prior work overlooks another mechanism that is at least equally important from a strategic standpoint: the ability of a firm to acquire another firm in the network and inherit its network ties. Such 'node collapse' can radically restructure the network in a single transaction, constituting a revolutionary change compared to the more evolutionary effect of tie additions and deletions. Moreover, acquisitions occur in highly competitive markets, making it crucial to account for the fact that multiple firms may simultaneously seek to reach advantageous network positions. We explore how these issues affect the dynamics of the network at the firm and industry levels through a simulation in which actors acquire one another to span more structural holes. We find that acquisition-driven network change affects the distribution of individual firms' performance and the structural properties of the industry-wide network.

This paper explores the emotional impact of prior performance on strategic choice. In addition to problemistic search considerations, performance feedback can have a significant impact on the emotional states of the strategic decision makers in the firms. This can in turn cause major, predictable effects on their strategic search and choice behaviors. The paper focuses on one such mechanism, based on robust findings from cognitive psychology and cognitive neuroscience. When applied to the context of performance feedback, this mechanism is able to generate novel predictions about impact of attainment discrepancy, as well as prior performance volatility, on the distance of strategic search and choice. Empirical support is found for these predictions when tested in the context of M&A decision making using a large panel dataset.

In this paper, we undertake a treatment of strategic interactions where players have potentially inconsistent mental models. A primary purpose of this paper is to direct attention to the centrality of mental models for analyses of strategic interaction. Conventional treatments of strategic interaction in the strategy and economics literatures assume a strong form of common knowledge among the players in which the players' mental models are essentially consistent and any differences among the players are handled as private information. This approach has been enormously valuable for understanding strategic interaction and has significant advantages in terms of limiting the set of possibilities to model and in terms of structuring the analysis. But, in settings where mental models are non-trivially inconsistent, the conventional approach may only awkwardly be adapted to capture the essence of the interaction. More importantly, players with inconsistent mental models commonly are unaware of critical aspects of the inconsistency: in such cases the common knowledge foundation of the conventional approach is undermined. For these reasons, models based on the conventional approach will sometimes prove misleading or offer inferior explanations to those offered by an inconsistent mental models approach.

In this paper we study creativity in strategy formulation. We use constructs from cognitive psychology, cognitive neuroscience and linguistics on categorization to model cognitive long jumps in strategy formulation. One such long jump is used as our motivating example– the “financial supermarkets” strategy created by Charles Merrill in 1940 which led to the dramatic rise of Merrill Lynch. By studying this case in detail using historical data and applying to it insights from the work on categorization, we obtain a better understanding of how Merrill might have thought about and arrived at his novel strategy. We then develop a formal model of this process and use it to generate generalizable propositions about performing cognitive long jumps.